The vast majority of conventional aircraft prognosis and diagnosis assumes that the sensor-wiring-processor pipeline is operating correctly and instead focuses on detecting failures of the components that are being measured. Generally where sensor failure is addressed the sensor fault detection falls into one of four categories such as knowledge driven methods, estimation methods, time-series analysis based methods, and machine learning based methods. Knowledge driven rely strictly on domain knowledge in order to pace rules/constraints on the sensor values. This class of methods can often detect more subtle faults, with “low intensity” signatures than the other three categories noted above however, because the knowledge-driven methods are not data driven they tend to be less robust in that they exhibit more false negatives. Estimation methods utilize the signals from unique, but correlated sensors in order to detect abnormalities. While these estimation methods do not necessarily require completely redundant sensors, they do require that a significantly strong correlation exist between two different sensors at the very least and that the fault does not exist far enough downstream from the sensors such that both sets of recorded measurements (sensor signals) are affected. These downstream faults that affect both sensor signals can happen, for example, if there is a fault at the wiring or processor stage. Time-series analysis based methods make use of temporal correlations among current and past measurements from a single sensor in order to predict future measurements. The time-series analysis based methods rely on a pre-defined fixed model structure with adaptable parameters, such as the well-known Autoregressive-Moving-Average (ARMA) class of models, however, the time-series analysis based methods are less robust than machine learning approaches due to their reliance on a fixed, pre-defined model. Machine learning based approaches infer a model of normal versus abnormal sensor measurements using training data, and then statistically detect and identify classes of faults. This is the most robust class of fault detection methods owing to its purely data-driven approach. However, the machine learning based approaches require the most data to train and tend to be less capable of picking up on failures that induce subtle “low intensity” signals.
Most conventional diagnostic algorithms onboard vehicles, such as aircraft, only issue fault messages when recorded sensor measurements enter abnormal ranges or exhibit “wild” dynamics. As such, most of the conventional fault diagnostic algorithms, such as those included in the fault detection methods described above, will not issue appropriate fault messages in response, and therefore vehicle operators and maintenance crew remain unaware that there is an impending failure.